Deep neural networks have achieved remarkable achievements in various machine learning fields, including image classification [1]}, [2]}, semantic segmentation [3]}, and object recognition [4]}. However, recent achievements heavily relied on large-scale datasets under the major assumption that the training and testing data have a consistent feature distribution. While in practice, the data collected from various scenes may suffer from different lighting conditions, color saturation, hue, visual angles, and background environments, resulting in domain gaps between training and testing data. Aiming at the above problems, domain adaptation can map data from different distributions of source and target domains into a feature space, and optimize the corresponding model parameters to make the distance of features from different domains as close as possible in this space.
According to the existence of labels in the target domain, domain adaptation can be divided into supervised domain adaptation, semi-supervised domain adaptation and unsupervised domain adaptation (UDA) [5]}. Since data labeling is often time-consuming, labor-intensive, and expensive, obtaining high-quality labels has always been an important factor limiting performance improvement. Compared with supervised and semi-supervised methods, UDA does not require any labeled target domain data and has more potential application scenarios, which deserves further attention. Therefore, in this paper, we focus on exploring UDA method aiming to transfer knowledge from a labeled source domain to a different unlabeled target domain.
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